Add-on astragalus in type 2 diabetes and chronic kidney disease: A multi-center, assessor-blind, randomized controlled trial.

BACKGROUND: Diabetes leads to chronic kidney disease (CKD) and kidney failure, requiring dialysis or transplantation. Astragalus, a common herbal medicine and US pharmacopeia-registered food ingredient, is shown kidney protective by retrospective and preclinical data but with limited long-term prospective clinical evidence. This trial aimed to assess the effectiveness of astragalus on kidney function decline in macroalbuminuric diabetic CKD patients. METHODS: This randomized, assessor-blind, standard care-controlled, multi-center clinical trial randomly assigned 118 patients with estimated glomerular filtration rate (eGFR) of 30-90 ml/min/1.73m2 and urinary albumin-to-creatinine ratio (UACR) of 300-5000 mg/g from 7 public outpatient clinics and the community in Hong Kong between July 2018 and April 2022 to add-on oral astragalus granules (15 gs of raw herbs daily equivalent) or to continue standard care alone as control for 48 weeks. Primary outcomes were the slope of change of eGFR (used for sample size calculation) and UACR of the intention-to-treat population. Secondary outcomes included endpoint blood pressures, biochemistry, biomarkers, concomitant drug change and adverse events. (ClinicalTrials.gov: NCT03535935) RESULTS: During the 48-week period, the estimated difference in the slope of eGFR decline was 4.6 ml/min/1.73m2 per year (95 %CI: 1.5 to 7.6, p = 0.003) slower with astragalus. For UACR, the estimated inter-group proportional difference in the slope of change was insignificant (1.14, 95 %CI: 0.85 to 1.52, p = 0.392). 117 adverse events from 31 astragalus-treated patients and 41 standard care-controlled patients were documented. The 48-week endpoint systolic blood pressure was 7.9 mmHg lower (95 %CI: -12.9 to -2.8, p = 0.003) in the astragalus-treated patients. 113 (96 %) and 107 (91 %) patients had post-randomization and endpoint primary outcome measures, respectively. CONCLUSION: In patients with type 2 diabetes, stage 2 to 3 CKD and macroalbuminuria, add-on astragalus for 48 weeks further stabilized kidney function on top of standard care.

Efficacy, safety and response predictors of adjuvant astragalus for diabetic kidney disease (READY): study protocol of an add-on, assessor-blind, parallel, pragmatic randomised controlled trial.

INTRODUCTION: Diabetic kidney disease (DKD) is a prevalent and costly complication of diabetes with limited therapeutic options, being the leading cause of end-stage kidney disease in most developed regions. Recent big data studies showed that add-on Chinese medicine (CM) led to a reduced risk of end-stage kidney disease and mortality among patients with chronic kidney disease (CKD) and diabetes. Astragalus, commonly known as huang-qi, is the most prescribed CM or used dietary herb in China for diabetes and DKD. In vivo and in vitro studies showed that astragalus ameliorated podocyte apoptosis, foot process effacement, mesangial expansion, glomerulosclerosis and interstitial fibrosis. Nevertheless, the clinical effect of astragalus remains uncharacterised. This pragmatic clinical trial aims to evaluate the effectiveness of add-on astragalus in patients with type 2 diabetes, stage 2-3 CKD and macroalbuminuria, and to identify related response predictors. METHODS AND ANALYSIS: This is an add-on, assessor-blind, parallel, pragmatic randomised controlled clinical trial. 118 patients diagnosed with DKD will be recruited and randomised 1:1 to receive 48 weeks of add-on astragalus or standard medical care. Primary endpoints are the changes in estimated glomerular filtration rate and urine albumin-to-creatinine ratio between baseline and treatment endpoint. Secondary endpoints include adverse events, fasting blood glucose, glycated haemoglobin, lipids and other biomarkers. Adverse events are monitored through self-complete questionnaire and clinical visits. Outcomes will be analysed by regression models. Subgroup and sensitivity analyses will be conducted for different epidemiological subgroups and statistical analyses. Enrolment started in July 2018. ETHICS AND DISSEMINATION: This study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West/East/Kowloon Central clusters (UW 16-553/HKEC-2019-026/REC (KC/KE)-19-0049/ER-4). We will report the findings in medical journals and conferences. The dataset will be available on reasonable request. TRIAL REGISTRATION NUMBER: NCT03535935.

Diagnostic assessment of a deep learning system for detecting atrial fibrillation in pulse waveforms.

OBJECTIVE: To evaluate the diagnostic performance of a deep learning system for automated detection of atrial fibrillation (AF) in photoplethysmographic (PPG) pulse waveforms. METHODS: We trained a deep convolutional neural network (DCNN) to detect AF in 17 s PPG waveforms using a training data set of 149 048 PPG waveforms constructed from several publicly available PPG databases. The DCNN was validated using an independent test data set of 3039 smartphone-acquired PPG waveforms from adults at high risk of AF at a general outpatient clinic against ECG tracings reviewed by two cardiologists. Six established AF detectors based on handcrafted features were evaluated on the same test data set for performance comparison. RESULTS: In the validation data set (3039 PPG waveforms) consisting of three sequential PPG waveforms from 1013 participants (mean (SD) age, 68.4 (12.2) years; 46.8% men), the prevalence of AF was 2.8%. The area under the receiver operating characteristic curve (AUC) of the DCNN for AF detection was 0.997 (95% CI 0.996 to 0.999) and was significantly higher than all the other AF detectors (AUC range: 0.924-0.985). The sensitivity of the DCNN was 95.2% (95% CI 88.3% to 98.7%), specificity was 99.0% (95% CI 98.6% to 99.3%), positive predictive value (PPV) was 72.7% (95% CI 65.1% to 79.3%) and negative predictive value (NPV) was 99.9% (95% CI 99.7% to 100%) using a single 17 s PPG waveform. Using the three sequential PPG waveforms in combination (<1 min in total), the sensitivity was 100.0% (95% CI 87.7% to 100%), specificity was 99.6% (95% CI 99.0% to 99.9%), PPV was 87.5% (95% CI 72.5% to 94.9%) and NPV was 100% (95% CI 99.4% to 100%). CONCLUSIONS: In this evaluation of PPG waveforms from adults screened for AF in a real-world primary care setting, the DCNN had high sensitivity, specificity, PPV and NPV for detecting AF, outperforming other state-of-the-art methods based on handcrafted features.

Diagnostic performance of an automatic blood pressure measurement device, Microlife WatchBP Home A, for atrial fibrillation screening in a real-world primary care setting.

OBJECTIVE: To evaluate the diagnostic performance of a UK National Institute for Health and Care Excellence-recommended automatic oscillometric blood pressure (BP) measurement device incorporated with an atrial fibrillation (AF) detection algorithm (Microlife WatchBP Home A) for real-world AF screening in a primary healthcare setting. SETTING: Primary healthcare setting in Hong Kong. INTERVENTIONS: This was a prospective AF screening study carried out between 1 September 2014 and 14 January 2015. The Microlife device was evaluated for AF detection and compared with a reference standard of lead-I ECG. PRIMARY OUTCOME MEASURES: Diagnostic performance of Microlife for AF detection. RESULTS: 5969 patients (mean age: 67.2±11.0 years; 53.9% female) were recruited. The mean CHA2DS2-VASc ( C : congestive heart failure [1 point]; H : hypertension [1 point]; A2 : age 65-74 years [1 point] and age ≥75 years [2 points]; D : diabetes mellitus [1 point]; S : prior stroke or transient ischemic attack [2 points]; VA : vascular disease [1 point]; and Sc : sex category [female] [1 point])score was 2.8±1.3. AF was diagnosed in 72 patients (1.21%) and confirmed by a 12-lead ECG. The Microlife device correctly identified AF in 58 patients and produced 79 false-positives. The corresponding sensitivity and specificity for AF detection were 80.6% (95% CI 69.5 to 88.9) and 98.7% (95% CI 98.3 to 98.9), respectively. Among patients with a false-positive by the Microlife device, 30.4% had sinus rhythm, 35.4% had sinus arrhythmia and 29.1% exhibited premature atrial complexes. With the low prevalence of AF in this population, the positive and negative predictive values of Microlife device for AF detection were 42.4% (95% CI 34.0 to 51.2) and 99.8% (95% CI 99.6 to 99.9), respectively. The overall diagnostic performance of Microlife device to detect AF as determined by area under the curves was 0.90 (95% CI 0.89 to 0.90). CONCLUSIONS: In the primary care setting, Microlife WatchBP Home was an effective means to screen for AF, with a reasonable sensitivity of 80.6% and a high negative predictive value of 99.8%, in addition to its routine function of BP measurement. In a younger patient population aged <65 years with a lower prevalence of AF, Microlife WatchBP Home A demonstrated a similar diagnostic accuracy.

Diagnostic Performance of a Smartphone-Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting.

BACKGROUND: Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera-based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real-world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting. METHODS AND RESULTS: Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single-lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI] 77-99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51-87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI: 97-99%] versus 99.4% [95% CI 99-100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38-67%] versus 76.9% [95% CI 56-91%]); both had a very high negative predictive value (99.8% [95% CI 99-100%] versus 99.2% [95% CI 98-100%]). CONCLUSIONS: The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population-based screening for AF.